Yihua Zhang

vanishing_me.jpg

Room 3210

428 S Shaw LN

East Lansing, Michigan

United States of America

I am Yihua Zhang (张逸骅), a second-year Ph.D. student from OPTML Group at Michigan State University, supervised by Prof. Sijia Liu. My research focuses on the trustworthy and scalable ML algorithms. In general, my research spans the areas of machine learning (ML)/deep learning (DL), optimization theory, computer vision, and security. These research topics provide a solid foundation for my current and future research: Making AI system responsible and efficient. My research on these two goals are intervened and can be summarized as the following two perspectives:

:heavy_check_mark: Algorithmic perspective: This line of research designs the scalable and theoretically-grounded machine learning algorithms subject to real-life constraints, e.g., computation/communication overhead, robustness, fairness, and interpretability.

:heavy_check_mark: Application perspective: This line of research tackles the domain-specific challenges to achieve scalable and trustworthy AI, e.g., robustness enhancement, fairness promotion, data privacy protection, and model compression.

news

Sep 22, 2023 :tada: One first-authored papers accepted in NeurIPS 2023! Paper and codes will come soon!
Aug 3, 2023 :tada: Our survey paper An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning has been made public on arxiv!
Jul 13, 2023 :tada: One first-authored paper accepted by ICCV’23!
May 19, 2023 :tada: I am honored to be selected to be the CVPR 2023 Outstanding Reviewer (232/7000+)!
Apr 24, 2023 :tada: One paper accepted in ICML 2023!

First-Authored Publications

See a full publication list at here.

  1. NeurIPS’23
    Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
    Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, and Sijia Liu
    In Thirty-seventh Conference on Neural Information Processing Systems 2023
  2. arxiv
    An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning
    Yihua Zhang, Prashant Khanduri, Ioannis Tsaknakis, Yuguang Yao, Mingyi Hong, and Sijia Liu
    In arxiv 2308.00788 Aug 2023
  3. ICCV’23
    Robust Mixture-of-Expert Training for Convolutional Neural Networks
    Yihua Zhang, Ruisi Cai, Tianlong Chen, Guanhua Zhang, Huan Zhang, Pin-Yu Chen, Shiyu Chang, Zhangyang Wang, and Sijia Liu
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Oct 2023
  4. ICLR’23
    What Is Missing in IRM Training and Evaluation? Challenges and Solutions
    Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush Varshney, and Sijia Liu
    In Eleventh International Conference on Learning Representations Oct 2023
  5. NeurIPS’22
    Advancing Model Pruning via Bi-level Optimization
    Yihua Zhang*, Yuguang Yao*, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, and Sijia Liu
    In Thirty-sixth Conference on Neural Information Processing Systems Oct 2022
  6. NeurIPS’22
    Fairness Reprogramming
    Guanhua Zhang*, Yihua Zhang*, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, and Shiyu Chang
    In Thirty-sixth Conference on Neural Information Processing Systems Oct 2022
  7. ICML’22
    Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
    Yihua Zhang*, Guanhua Zhang*, Prashant Khanduri, Mingyi Hong, Shiyu Chang, and Sijia Liu
    In Proceedings of the 39th International Conference on Machine Learning Oct 2022
  8. CVPR’22
    Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
    Tianlong Chen*, Zhenyu Zhang*, Yihua Zhang*, Shiyu Chang, Sijia Liu, and Zhangyang Wang
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Oct 2022